{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from osgeo import gdal,ogr,gdal_array,gdalnumeric\n",
    "import shapely.wkt\n",
    "import shapely.affinity\n",
    "from tqdm import tqdm\n",
    "import pandas as pd\n",
    "from pandas import set_option\n",
    "import numpy as np\n",
    "import cv2\n",
    "from collections import Counter\n",
    "import os\n",
    "import osr\n",
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt\n",
    "from geopandas import *\n",
    "# import rasterio as rio\n",
    "# import rasterio.mask\n",
    "from sklearn import model_selection\n",
    "import pickle \n",
    "# from sklearn.metrics import classification_report\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#定义读取矢量函数\n",
    "def read_shp(shp_path):    \n",
    "    vector = gpd.read_file(shp_path)\n",
    "    return vector\n",
    "#定义读取栅格函数\n",
    "def read_raster(raster_path):\n",
    "    \n",
    "    data = gdal.Open(raster_path)\n",
    "    im_width = data.RasterXSize    #栅格矩阵的列数\n",
    "    im_height = data.RasterYSize   #栅格矩阵的行数        \n",
    "    gt = data.GetGeoTransform()#仿射矩阵\n",
    "    crs = data.GetProjection()#地图投影信息\n",
    "    data_array = data.ReadAsArray(0,0,im_width,im_height)#转化为矩阵\n",
    "    return data,data_array,gt,crs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#将面状要素\n",
    "def geo2pixel(record,GeoTransform):\n",
    "    \"\"\"\n",
    "    http://www.gdal.org/gdal_tutorial.html\n",
    "\n",
    "    GeoTransform[0] /* top left x */\n",
    "    GeoTransform[1] /* w-e pixel resolution */\n",
    "    GeoTransform[2] /* 0 */\n",
    "    GeoTransform[3] /* top left y */\n",
    "    GeoTransform[4] /* 0 */\n",
    "    \"\"\"\n",
    "    uper_left_x = float(GeoTransform[0])\n",
    "    uper_left_y = float(GeoTransform[3])\n",
    "    pixel_width = float(GeoTransform[1])\n",
    "    pixel_height = float(GeoTransform[5])\n",
    "    (mx, my) = (record[0], record[1])\n",
    "    px = int((mx - uper_left_x) / pixel_width)  # x pixel\n",
    "    py = int((my - uper_left_y) / pixel_height)  # y pixel\n",
    "    return px,py\n",
    "#根据矢量生成mask,再根据mask提取图像信息\n",
    "def clip(poly,gt,data):\n",
    "    \n",
    "    minX,maxX = min(poly.envelope.exterior.coords.xy[0]),max(poly.envelope.exterior.coords.xy[0])\n",
    "    minY,maxY = min(poly.envelope.exterior.coords.xy[1]),max(poly.envelope.exterior.coords.xy[1])\n",
    "\n",
    "    uper_left_X, uper_left_Y = geo2pixel((minX,maxY),gt)\n",
    "    lower_right_X, lower_right_Y = geo2pixel((maxX,minY),gt)\n",
    "\n",
    "    pxWidth = int(lower_right_X - uper_left_X)\n",
    "    pxHeight = int(lower_right_Y - uper_left_Y)\n",
    "    #根据外接矩形裁剪\n",
    "    clip = data.ReadAsArray(int(uper_left_X),int(uper_left_Y),int(pxWidth),int(pxHeight))\n",
    "\n",
    "    gt2 = list(gt)\n",
    "    gt2[0] = minX\n",
    "    gt2[3] = maxY\n",
    "    ext_pix = np.array([geo2pixel(points,gt2) for points in poly.exterior.coords])\n",
    "    ext_pix = np.expand_dims(ext_pix,axis=0)\n",
    "    mask = np.ones([pxHeight,pxWidth], np.uint8)\n",
    "\n",
    "    mask = cv2.fillPoly(mask,ext_pix, 0)#介绍CV2.fillploy()\n",
    "    clip = gdalnumeric.choose(mask, (clip, 0))\n",
    "    return clip\n",
    "#保存tif文件函数\n",
    "def writeTiff(im_data,im_geotrans,im_proj,path):\n",
    "    if 'int8' in im_data.dtype.name:\n",
    "        datatype = gdal.GDT_Byte\n",
    "    elif 'int16' in im_data.dtype.name:\n",
    "        datatype = gdal.GDT_UInt16\n",
    "    else:\n",
    "        datatype = gdal.GDT_Float32\n",
    "    if len(im_data.shape) == 3:\n",
    "        im_bands, im_height, im_width = im_data.shape\n",
    "    elif len(im_data.shape) == 2:\n",
    "        im_data = np.array([im_data])\n",
    "        im_bands, im_height, im_width = im_data.shape\n",
    "#创建文件\n",
    "    driver = gdal.GetDriverByName(\"GTiff\")\n",
    "    dataset = driver.Create(path, int(im_width), int(im_height), int(im_bands), datatype)\n",
    "    if(dataset!= None):\n",
    "        dataset.SetGeoTransform(im_geotrans) #写入仿射变换参数\n",
    "        dataset.SetProjection(im_proj) #写入投影\n",
    "    for i in range(im_bands):\n",
    "        dataset.GetRasterBand(i+1).WriteArray(im_data[i])\n",
    "    del dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#把所有根据矢量样本提取的特征参数统一为能够训练的标准格式\n",
    "def concat_clip_data(shp_files,raster):\n",
    "    shp_list = [i for i in shp_files if i.endswith('.shp')== True]\n",
    "    out = []\n",
    "    for i in shp_list:\n",
    "        vector = read_shp(os.path.join(r'./使用数据/分类矢量',i))\n",
    "        #\n",
    "        class_ = vector['class'].unique()\n",
    "\n",
    "        all_list = []\n",
    "        for j in range(len(vector)):\n",
    "\n",
    "            if j >= len(vector) or j < -j:\n",
    "                continue\n",
    "            ploy = vector.iloc[j]['geometry']\n",
    "            clip_array = clip(ploy,gt,raster)\n",
    "            #判断裁剪后的结果的维度是否符合（w,h,channel）的格式\n",
    "            if clip_array.ndim != 3:\n",
    "                continue\n",
    "            lst = []\n",
    "            for n in range(clip_array.ndim+1):\n",
    "                b = pd.DataFrame(clip_array[n].flatten())\n",
    "    #             print(len(b),type(b))\n",
    "                lst.append(b)\n",
    "\n",
    "            lab = pd.DataFrame([class_]*(clip_array.shape[1]*clip_array.shape[2]))\n",
    "            lst.append(lab)\n",
    "            ds = pd.concat(lst,axis=1,ignore_index=True)\n",
    "\n",
    "            all_list.append(ds)\n",
    "        all_ds = pd.concat(all_list,axis=0,ignore_index=True)\n",
    "\n",
    "        out.append(all_ds)\n",
    "    out = pd.concat(out,axis=0,ignore_index=True)\n",
    "    # print(out.head(-10))\n",
    "    # print(len(out))\n",
    "    return out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "开始训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['DAOLU.shp', 'JUMINQU.shp', 'LUODI.shp', 'NONGTIAN.shp', 'SENLIN.shp', 'SHUIYU.shp']\n"
     ]
    }
   ],
   "source": [
    "#定义当前工作目录\n",
    "work_path = r'./'\n",
    "#读取栅格影像\n",
    "data,data_array,gt,crs = read_raster(r'./使用数据/分类栅格/裁剪太子山影像.tif')\n",
    "#查看矢量文件\n",
    "shp_files = os.listdir(r'./使用数据/分类矢量')\n",
    "print([i for i in shp_files if i.endswith('.shp')== True])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "          0    1    2    3  4\n",
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     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#调用concat_clip_data函数对每个矢量提取的数据进行合并\n",
    "out = concat_clip_data(shp_files,data)\n",
    "#随机查看\n",
    "out.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "          0    1    2    3  4\n",
       "73512   434  325  299  337  4\n",
       "125642  386  255  166   88  6\n",
       "78438   394  274  202  276  4\n",
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     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#删除0填充的样本\n",
    "out_train = out.drop(out[(out[0] == 0) & (out[1] == 0)&(out[2] == 0)&(out[3] == 0)].index)\n",
    "#随机查看样本\n",
    "out_train.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = out_train.iloc[:,0:4] #选取前4个波段作为特征\n",
    "Y = out_train.iloc[:,-1]#最后一列为标签\n",
    "train_data,test_data,train_label,test_label = model_selection.train_test_split(X,Y, random_state=1, train_size=0.7,test_size=0.3)#将数据分割为训练集与测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "          0    1    2    3\n",
       "96426   357  227  141  249\n",
       "130759  383  247  156   86\n",
       "93110   369  235  153  263\n",
       "125652  388  255  164   88\n",
       "66104   396  270  195  268"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看训练集\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#数据标准化，提高模型泛化能力\n",
    "def data_processing(data):\n",
    "    ss_x = StandardScaler().fit(data)\n",
    "    StandardScaler_data = ss_x.transform(data)\n",
    "    return StandardScaler_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.25960453, -1.23282157, -1.0957493 , -0.0078631 ],\n",
       "       [-0.44826707, -0.70035567, -0.84736108, -2.03330566],\n",
       "       [-0.88514109, -1.01983521, -0.89703873,  0.16610129],\n",
       "       ...,\n",
       "       [ 1.39284488,  1.21652156,  1.30533683, -0.14454941],\n",
       "       [ 0.83114971,  1.0301585 ,  1.22254076,  0.47675199],\n",
       "       [-1.25960453, -1.49905452, -1.21166381, -0.66644258]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data = data_processing(train_data)\n",
    "test_data = data_processing(test_data)\n",
    "#查看标准化后的训练集\n",
    "train_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>band1</th>\n",
       "      <th>band2</th>\n",
       "      <th>band3</th>\n",
       "      <th>band4</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.259605</td>\n",
       "      <td>-1.232822</td>\n",
       "      <td>-1.095749</td>\n",
       "      <td>-0.007863</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.448267</td>\n",
       "      <td>-0.700356</td>\n",
       "      <td>-0.847361</td>\n",
       "      <td>-2.033306</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.885141</td>\n",
       "      <td>-1.019835</td>\n",
       "      <td>-0.897039</td>\n",
       "      <td>0.166101</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.292241</td>\n",
       "      <td>-0.487369</td>\n",
       "      <td>-0.714887</td>\n",
       "      <td>-2.008454</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.042598</td>\n",
       "      <td>-0.088020</td>\n",
       "      <td>-0.201552</td>\n",
       "      <td>0.228231</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34161</th>\n",
       "      <td>-0.105009</td>\n",
       "      <td>-0.167890</td>\n",
       "      <td>-0.598973</td>\n",
       "      <td>-1.983602</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34162</th>\n",
       "      <td>-1.384426</td>\n",
       "      <td>-1.392561</td>\n",
       "      <td>-1.211664</td>\n",
       "      <td>0.240657</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34163</th>\n",
       "      <td>1.392845</td>\n",
       "      <td>1.216522</td>\n",
       "      <td>1.305337</td>\n",
       "      <td>-0.144549</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34164</th>\n",
       "      <td>0.831150</td>\n",
       "      <td>1.030158</td>\n",
       "      <td>1.222541</td>\n",
       "      <td>0.476752</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34165</th>\n",
       "      <td>-1.259605</td>\n",
       "      <td>-1.499055</td>\n",
       "      <td>-1.211664</td>\n",
       "      <td>-0.666443</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>34166 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          band1     band2     band3     band4  label\n",
       "0     -1.259605 -1.232822 -1.095749 -0.007863      5\n",
       "1     -0.448267 -0.700356 -0.847361 -2.033306      6\n",
       "2     -0.885141 -1.019835 -0.897039  0.166101      5\n",
       "3     -0.292241 -0.487369 -0.714887 -2.008454      6\n",
       "4     -0.042598 -0.088020 -0.201552  0.228231      2\n",
       "...         ...       ...       ...       ...    ...\n",
       "34161 -0.105009 -0.167890 -0.598973 -1.983602      6\n",
       "34162 -1.384426 -1.392561 -1.211664  0.240657      5\n",
       "34163  1.392845  1.216522  1.305337 -0.144549      2\n",
       "34164  0.831150  1.030158  1.222541  0.476752      4\n",
       "34165 -1.259605 -1.499055 -1.211664 -0.666443      5\n",
       "\n",
       "[34166 rows x 5 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for_train_data = pd.DataFrame(train_data)\n",
    "for_train_data['label']=train_label.values\n",
    "for_train_data=for_train_data.rename(columns={0:'band1',1:'band2',2:'band3',3:'band4'})\n",
    "for_train_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 保存模型\n",
    "def save_model(save_path,model,model_name):\n",
    "    #以读二进制的方式打开文件\n",
    "    file = open(os.path.join(save_path,model_name), \"wb\")\n",
    "    #将模型写入文件：\n",
    "    pickle.dump(model, file)\n",
    "    #最后关闭文件：\n",
    "    file.close()\n",
    "    return \n",
    "# 读取模型\n",
    "def load_model(model_path,model_name):\n",
    "    #以读二进制的方式打开文件\n",
    "    file = open(os.path.join(model_path,model_name), \"rb\")\n",
    "    #把模型从文件中读取出来\n",
    "    model = pickle.load(file)\n",
    "    #关闭文件\n",
    "    file.close()\n",
    "    return\n",
    "#预测\n",
    "def predict_img(model_path,raster_Path,out_path):\n",
    "\n",
    "    data,data_array,gt,crs = read_raster(raster_Path)\n",
    "\n",
    "    #调用保存好的模型\n",
    "    #以读二进制的方式打开文件\n",
    "    file = open(model_path, \"rb\")\n",
    "    #把模型从文件中读取出来\n",
    "    model = pickle.load(file)\n",
    "    #关闭文件\n",
    "    file.close()\n",
    "    #用读入的模型进行预测\n",
    "    #  在与测试前要调整一下数据的格式\n",
    "    array = np.zeros((data_array.shape[0],data_array.shape[1]*data_array.shape[2]))\n",
    "    for i in range(data_array.shape[0]):\n",
    "        array[i] = data_array[i].flatten() \n",
    "    array = array.swapaxes(0,1)\n",
    "    #  对调整好格式的数据进行预测\n",
    "    array = data_processing(array)\n",
    "    pred = model.predict(array)\n",
    "    #  同样地，我们对预测好的数据调整为我们图像的格式\n",
    "    # pred = pred.reshape(Landset_data.shape[1],Landset_data.shape[2])*255\n",
    "    pred = pred.reshape(data_array.shape[1],data_array.shape[2])\n",
    "    pred = pred.astype(np.uint8)\n",
    "\n",
    "    #  将结果写到tif图像里\n",
    "    writeTiff(pred,gt,crs,out_path)\n",
    "    return pred\n",
    "\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['font.sans-serif'] =[ 'SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集： 0.9977755663525142\n",
      "测试集： 0.9087618657378952\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#构建随机森林分类模型\n",
    "RF_classifier = RandomForestClassifier(n_estimators=100, \n",
    "                               bootstrap = True,\n",
    "                               max_features = 'sqrt')\n",
    "RF_classifier.fit(train_data, train_label.ravel())#ravel函数将数据拉伸到一维\n",
    "\n",
    "\n",
    "# 计算准确率\n",
    "print(\"训练集：\",RF_classifier.score(train_data,train_label))\n",
    "print(\"测试集：\",RF_classifier.score(test_data,test_label))\n",
    "\n",
    "# 保存模型\n",
    "save_model(os.path.join(work_path,'输出数据'),RF_classifier,'model_rf.pickle')\n",
    "\n",
    "#预测模型\n",
    "model_path = os.path.join(work_path,'输出数据','model_rf.pickle')\n",
    "raster_Path = os.path.join(work_path,'使用数据','分类栅格','裁剪太子山影像.tif')\n",
    "out_path = os.path.join(work_path,'输出数据','pred_rf.tif')\n",
    "pred_img = predict_img(model_path,raster_Path,out_path)\n",
    "#查看分类情况\n",
    "# plt.text(x = -10,y = -30, s = '行')\n",
    "# plt.text(x = 660,y = 720, s = '列')\n",
    "plt.axis('off')\n",
    "plt.imshow(pred_img)\n",
    "plt.savefig(r'g:\\python林业资源\\出图/分类RF.png',dpi=300)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#调用sklearn库中的指标求解\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "from sklearn.metrics import average_precision_score\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#给出分类结果\n",
    "y_pred = RF_classifier.predict(test_data)\n",
    "y_true = test_label\n",
    "print(\"accuracy_score:\", accuracy_score(y_true, y_pred))\n",
    "print(\"precision_score:\", metrics.precision_score(y_true, y_pred, average='micro'))\n",
    "print(\"recall_score:\", metrics.recall_score(y_true, y_pred, average='micro'))\n",
    "print(\"f1_score:\", metrics.f1_score(y_true, y_pred, average='micro'))\n",
    "print(\"f0.5_score:\", metrics.fbeta_score(y_true, y_pred, beta=0.5, average='micro'))\n",
    "print(\"f2_score:\", metrics.fbeta_score(y_true, y_pred, beta=2.0, average='micro'))\n",
    "print(classification_report(y_true=y_true, y_pred=y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#构建K邻域分类模型\n",
    "KNN_classifier = neighbors.KNeighborsClassifier(algorithm='kd_tree')\n",
    "KNN_classifier.fit(train_data, train_label.ravel())\n",
    "# 计算准确率\n",
    "print(\"训练集：\",KNN_classifier.score(train_data,train_label))\n",
    "print(\"测试集：\",KNN_classifier.score(test_data,test_label))\n",
    "\n",
    "# 保存模型\n",
    "save_model(os.path.join(work_path,'输出数据'),KNN_classifier,'model_knn.pickle')\n",
    "#预测模型\n",
    "model_path = os.path.join(work_path,'输出数据','model_knn.pickle')\n",
    "raster_Path = os.path.join(work_path,'使用数据','分类栅格','裁剪太子山影像.tif')\n",
    "out_path = os.path.join(work_path,'输出数据','pred_knn.tif')\n",
    "pred_img = predict_img(model_path,raster_Path,out_path)\n",
    "#查看分类情况\n",
    "plt.axis('off')\n",
    "plt.imshow(pred_img)\n",
    "# plt.savefig(r'F:\\python林业资源\\出图/分类KNN.png',dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#给出分类结果\n",
    "y_pred = KNN_classifier.predict(test_data)\n",
    "y_true = test_label\n",
    "print(\"accuracy_score:\", accuracy_score(y_true, y_pred))\n",
    "print(\"precision_score:\", metrics.precision_score(y_true, y_pred, average='micro'))\n",
    "print(\"recall_score:\", metrics.recall_score(y_true, y_pred, average='micro'))\n",
    "print(\"f1_score:\", metrics.f1_score(y_true, y_pred, average='micro'))\n",
    "print(\"f0.5_score:\", metrics.fbeta_score(y_true, y_pred, beta=0.5, average='micro'))\n",
    "print(\"f2_score:\", metrics.fbeta_score(y_true, y_pred, beta=2.0, average='micro'))\n",
    "print(classification_report(y_true=y_true, y_pred=y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#构建决策树分类模型\n",
    "Tree_classifier = tree.DecisionTreeClassifier(criterion='entropy')\n",
    "Tree_classifier.fit(train_data, train_label.ravel())\n",
    "# 计算准确率\n",
    "print(\"训练集：\",Tree_classifier.score(train_data,train_label))\n",
    "print(\"测试集：\",Tree_classifier.score(test_data,test_label))\n",
    "# 保存模型\n",
    "save_model(os.path.join(work_path,'输出数据'),Tree_classifier,'model_tree.pickle')\n",
    "#预测模型\n",
    "model_path = os.path.join(work_path,'输出数据','model_tree.pickle')\n",
    "raster_Path = os.path.join(work_path,'使用数据','分类栅格','裁剪太子山影像.tif')\n",
    "out_path = os.path.join(work_path,'输出数据','pred_tree.tif')\n",
    "pred_img = predict_img(model_path,raster_Path,out_path)\n",
    "#查看分类情况\n",
    "plt.axis('off')\n",
    "plt.imshow(pred_img)\n",
    "# plt.savefig(r'F:\\python林业资源\\出图/分类决策树.png',dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#给出分类结果\n",
    "y_pred = Tree_classifier.predict(test_data)\n",
    "y_true = test_label\n",
    "print(\"accuracy_score:\", accuracy_score(y_true, y_pred))\n",
    "print(\"precision_score:\", metrics.precision_score(y_true, y_pred, average='micro'))\n",
    "print(\"recall_score:\", metrics.recall_score(y_true, y_pred, average='micro'))\n",
    "print(\"f1_score:\", metrics.f1_score(y_true, y_pred, average='micro'))\n",
    "print(\"f0.5_score:\", metrics.fbeta_score(y_true, y_pred, beta=0.5, average='micro'))\n",
    "print(\"f2_score:\", metrics.fbeta_score(y_true, y_pred, beta=2.0, average='micro'))\n",
    "print(classification_report(y_true=y_true, y_pred=y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#构建持向量机分类模型\n",
    "SVM_classifier = svm.SVC()\n",
    "SVM_classifier.fit(train_data, train_label.ravel())\n",
    "# 计算准确率\n",
    "print(\"训练集：\",SVM_classifier.score(train_data,train_label))\n",
    "print(\"测试集：\",SVM_classifier.score(test_data,test_label))\n",
    "# 保存模型\n",
    "save_model(os.path.join(work_path,'输出数据'),SVM_classifier,'model_svm.pickle')\n",
    "#预测模型\n",
    "model_path = os.path.join(work_path,'输出数据','model_svm.pickle')\n",
    "raster_Path = os.path.join(work_path,'使用数据','分类栅格','裁剪太子山影像.tif')\n",
    "out_path = os.path.join(work_path,'输出数据','pred_svm.tif')\n",
    "pred_img = predict_img(model_path,raster_Path,out_path)\n",
    "#查看分类情况\n",
    "# plt.text(x = -10,y = -30, s = '行')\n",
    "# plt.text(x = 660,y = 720, s = '列')\n",
    "plt.axis('off')\n",
    "plt.imshow(pred_img)\n",
    "# plt.savefig(r'F:\\python林业资源\\出图/分类svm.png',dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#给出分类结果\n",
    "y_pred = SVM_classifier.predict(test_data)\n",
    "y_true = test_label\n",
    "print(\"accuracy_score:\", accuracy_score(y_true, y_pred))\n",
    "print(\"precision_score:\", metrics.precision_score(y_true, y_pred, average='micro'))\n",
    "print(\"recall_score:\", metrics.recall_score(y_true, y_pred, average='micro'))\n",
    "print(\"f1_score:\", metrics.f1_score(y_true, y_pred, average='micro'))\n",
    "print(\"f0.5_score:\", metrics.fbeta_score(y_true, y_pred, beta=0.5, average='micro'))\n",
    "print(\"f2_score:\", metrics.fbeta_score(y_true, y_pred, beta=2.0, average='micro'))\n",
    "print(classification_report(y_true=y_true, y_pred=y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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